This paper addresses the problem of classifying actions performed
by a human subject in a video sequence. A representation
eigenspace approach based on the visual appearance is
used to train the classifier. Before dimensionality reduction
exploiting the PCA/LLE algorithms, a high dimensional description
of each frame of the video sequence is constructed,
based on foreground blob analysis. The classification task is
performed by matching incrementally the reduced representation
of the test image sequence against each of the learned
ones, and accumulating matching scores until a decision is
obtained; to this aim, two different metrics are introduced
and evaluated. Experimental results demonstrate that the
approach is accurate enough and feasible for behavior classification.
Furthermore, we argue that the choice of both
the feature descriptor and the metric for the matching score
can dramatically influence the performance of the results.

This paper addresses the problem of classifying actions performed
by a human subject in a video sequence. A representation
eigenspace approach based on the visual appearance is
used to train the classifier. Before dimensionality reduction
exploiting the PCA/LLE algorithms, a high dimensional description
of each frame of the video sequence is constructed,
based on foreground blob analysis. The classification task is
performed by matching incrementally the reduced representation
of the test image sequence against each of the learned
ones, and accumulating matching scores until a decision is
obtained; to this aim, two different metrics are introduced
and evaluated. Experimental results demonstrate that the
approach is accurate enough and feasible for behavior classification.
Furthermore, we argue that the choice of both
the feature descriptor and the metric for the matching score
can dramatically influence the performance of the results.